The rapid digitalization of financial services in Bangladesh has increased the sector's vulnerability to cyber threats, making cybersecurity a critical concern. Traditional security measures are proving insufficient against evolving cyber risks, necessitating advanced technologies for effective threat detection and mitigation. Machine Learning (ML) has emerged as a powerful tool in enhancing cybersecurity by enabling real-time threat detection, anomaly detection, fraud prevention, and predictive risk assessment. This research explores the role of ML in enhancing cybersecurity in the financial services sector of Bangladesh. ML algorithms, such as supervised and unsupervised learning, deep learning, and reinforcement learning, are used to analyze vast amounts of transactional data, detect suspicious patterns, and automate security protocols. The study examines how financial institutions in Bangladesh leverage MLdriven cybersecurity solutions, including fraud detection systems, intrusion detection systems (IDS), behavioral analytics, and biometric authentication. Additionally, it evaluates the challenges of implementing ML-based cybersecurity, such as data privacy concerns, model interpretability, and regulatory compliance. Given the rising cyber threats, including phishing, ransomware, and identity theft, this research highlights the need for strong AI-driven security frameworks and collaborative industry-government initiatives to safeguard financial institutions in Bangladesh. The study aims to provide insights into the effectiveness of ML-based cybersecurity solutions and recommend strategies for financial organizations to strengthen their cyber resilience in an increasingly digital financial landscape.